Overview

Victoria at a Glance {data-height=140}

KPIs

6,977,755 +1.45% Melton

Interpretation

📝 Interpretation

  • Population Milestone — Victoria’s population reached 6,977,755 in 2024, reflecting continued recovery and strong migration inflows post-pandemic.
  • Sustained Growth — The state recorded an average growth rate of 1.45%, showing steady population expansion supported by both metropolitan and regional development.
  • Top Performer — Melton emerged as the fastest-growing LGA, highlighting the ongoing suburban expansion trend in Melbourne’s outer growth corridors.

References

References:

Population Growth

Row

Top 10 LGAs by % Growth

Top 10 LGAs by Absolute Growth (people)

Interpretation

The left chart compares population growth percentage-wise, while the right shows absolute increases. Together, they reveal that outer-metropolitan LGAs such as Melton, Wyndham, and Casey lead both in relative and total growth, reflecting Victoria’s ongoing suburban expansion.

Demographics

Female Share by Age Group

Chart

Interpretation

Interpretation

The chart shows that Melbourne consistently maintains a slightly higher female population share across most age groups compared to the rest of Victoria.
The largest difference is seen among younger working-age groups (25–34 years), reflecting Melbourne’s attraction for education and job opportunities.
Meanwhile, the Rest of Victoria records higher female shares in older age brackets, suggesting an aging regional demographic trend.

---
title: "Victoria Population Growth & Demographic Shifts (2023–24)"
author: "Albert Shaji (S4140500)"
date: "`r format(Sys.Date())`"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    theme: cosmo
    source_code: embed
---


```{r data_global, include=FALSE}
suppressPackageStartupMessages({
  library(tidyverse); library(readxl); library(janitor); library(scales); library(stringr); library(flexdashboard)
})

abs_xlsx <- "32180DS0002_2023-24.xlsx"
vic_sheet <- "Table 2"
raw <- read_excel(abs_xlsx, sheet = vic_sheet, col_names = FALSE)

# Find header row 
hdr_row <- which(apply(raw, 1, function(r) any(grepl("LGA", as.character(r), ignore.case = TRUE))))[1]
if (is.na(hdr_row)) hdr_row <- 6

headers  <- unlist(raw[hdr_row, ], use.names = FALSE)
abs_vic0 <- as_tibble(raw[(hdr_row + 1):nrow(raw), , drop = FALSE])
names(abs_vic0) <- make_clean_names(headers)

nm <- names(abs_vic0)

# LGA column
lga_col <- nm[grepl("(^lga$|^lga_?name|lga.*name)", nm, ignore.case = TRUE)][1]
if (is.na(lga_col)) {
  nonnum_idx <- which(sapply(abs_vic0, function(x) any(grepl("[A-Za-z]", as.character(x)))))[1]
  lga_col <- if (!is.na(nonnum_idx)) nm[nonnum_idx] else nm[min(2, length(nm))]
}

# ERP columns
erp23_col <- if ("no"   %in% nm) "no"   else if ("no_3" %in% nm) "no_3" else NA_character_
erp24_col <- if ("no_2" %in% nm) "no_2" else if ("no_4" %in% nm) "no_4" else NA_character_
if (is.na(erp23_col) || is.na(erp24_col)) {
  cand <- nm[!grepl("code|name|percent|per_cent|km2|density|area|note", nm, TRUE)]
  cand <- cand[sapply(abs_vic0[cand], function(x){
    y <- suppressWarnings(as.numeric(gsub("[^0-9.-]", "", as.character(x))))
    mean(!is.na(y)) > 0.6
  })]
  after_lga_idx <- match(lga_col, nm)
  cand <- cand[match(cand, nm) > after_lga_idx]
  if (length(cand) >= 2) { erp23_col <- cand[1]; erp24_col <- cand[2] }
}

num <- function(x) suppressWarnings(as.numeric(gsub("[^0-9.-]", "", as.character(x))))

abs_vic <- abs_vic0 |>
  transmute(
    lga       = str_squish(as.character(.data[[lga_col]])),
    erp_2023  = num(.data[[erp23_col]]),
    erp_2024  = num(.data[[erp24_col]])
  ) |>
  filter(!is.na(lga), lga != "", !grepl("total|victoria", lga, TRUE),
         !is.na(erp_2023), !is.na(erp_2024), erp_2023 > 1000, erp_2024 > 1000) |>
  mutate(growth_n = erp_2024 - erp_2023,
         growth_pct = if_else(erp_2023 > 0, 100 * (erp_2024 - erp_2023) / erp_2023, NA_real_)) |>
  arrange(desc(growth_pct))

# KPIs used by valueBox()
total_pop_2024 <- sum(abs_vic$erp_2024, na.rm = TRUE)
avg_growth_pct <- mean(abs_vic$growth_pct, na.rm = TRUE)
top_lga        <- abs_vic$lga[which.max(abs_vic$growth_pct)]

demo_long <- NULL

demo_csv <- "Age and sex distribution, Victoria.csv"
if (file.exists(demo_csv)) {
  library(readr); library(janitor); library(stringr); library(dplyr); library(tidyr)

  demo_raw <- read_csv(demo_csv, skip = 1, show_col_types = FALSE) |> clean_names()
  cols <- names(demo_raw)

  # detect columns robustly
  age_col  <- cols[str_detect(cols, regex("^age_?group", TRUE))][1]
  melb_all <- cols[str_detect(cols, regex("melbourne", TRUE))]
  rest_all <- cols[str_detect(cols, regex("rest.*(vic|victoria)", TRUE))]

  pick_last <- function(x) if (length(x)) tail(x, 1) else NA_character_
  melb_f <- pick_last(melb_all[str_detect(melb_all, regex("female", TRUE))])
  rest_f <- pick_last(rest_all[str_detect(rest_all, regex("female", TRUE))])
  if (is.na(melb_f) && length(melb_all) >= 2) melb_f <- melb_all[2]
  if (is.na(rest_f) && length(rest_all) >= 2) rest_f <- rest_all[2]

  num <- function(x) suppressWarnings(as.numeric(gsub("[^0-9.-]", "", as.character(x))))

  if (!is.na(age_col) && !is.na(melb_f) && !is.na(rest_f)) {
    demo_long <- demo_raw |>
      transmute(
        age_group    = .data[[age_col]],
        Melbourne    = num(.data[[melb_f]]),
        `Rest of Vic`= num(.data[[rest_f]])
      ) |>
      pivot_longer(-age_group, names_to = "region", values_to = "female_pct") |>
      filter(!is.na(female_pct))
  }
}

```


# Overview

## Victoria at a Glance {data-height=140}   <!-- compact tile row -->
### KPIs

```{r}

valueBox(
  value = paste0(formatC(total_pop_2024, big.mark = ",", format = "f", digits = 0),
                 "  +", round(avg_growth_pct, 2), "%  ", top_lga),
  caption = "Total Population (2024)",
  color = "primary"
)



```


##  Interpretation 
### 📝 Interpretation 

- **Population Milestone** — Victoria’s population reached **`r scales::comma(total_pop_2024)`** in 2024, reflecting continued recovery and strong migration inflows post-pandemic.  
- **Sustained Growth** — The state recorded an average growth rate of **`r round(avg_growth_pct, 2)`%**, showing steady population expansion supported by both metropolitan and regional development.  
- **Top Performer** — **`r top_lga`** emerged as the fastest-growing LGA, highlighting the ongoing suburban expansion trend in Melbourne’s outer growth corridors.

## References
### References:

<div style="background-color:#eef5fb; border-left:5px solid #0073e6; border-radius:8px; padding:15px 25px; margin-top:15px; font-size:15px; line-height:1.6;">

- Australian Bureau of Statistics. (2024). *Regional population, 2023–24: Victoria (Catalogue No. 3218.0).*  
  Australian Government.  
  [https://www.abs.gov.au/statistics/people/population/regional-population/latest-release](https://www.abs.gov.au/statistics/people/population/regional-population/latest-release)

- Australian Bureau of Statistics. (2024). *Age and sex distribution, Victoria.*  
  Australian Government Data Portal.  
  [https://data.gov.au/dataset](https://data.gov.au/dataset)

</div>

 


# Population Growth
Row {.columns-2 data-height=800}
-------------------------------------

### Top 10 LGAs by % Growth

```{r}

abs_vic |>
  slice_max(growth_pct, n = 10) |>
  ggplot(aes(x = reorder(lga, growth_pct), y = growth_pct)) +
  geom_col(fill = "steelblue", width = 0.7) +
  coord_flip() +
  labs(x = NULL, y = "Growth (%)", title = "Top 10 LGAs by Population Growth (%)") +
  scale_y_continuous(labels = scales::label_percent(accuracy = 0.1, scale = 1)) +
  theme_minimal(base_size = 18) +
  theme(
    plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
    axis.text = element_text(size = 14),
    axis.title = element_text(size = 14)
  )


```


### Top 10 LGAs by Absolute Growth (people)



```{r}
abs_vic |>
slice_max(growth_n, n = 10) |>
ggplot(aes(x = reorder(lga, growth_n), y = growth_n)) +
geom_col(fill = "darkorange", width = 0.7) +
coord_flip() +
labs(x = NULL, y = "People added (2023 → 2024)", title = "Top 10 LGAs by Absolute Growth") +
scale_y_continuous(labels = scales::label_number(big.mark = ",")) +
theme_minimal(base_size = 18) +
theme(
plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
axis.text = element_text(size = 14),
axis.title = element_text(size = 14)
)


```

## Interpretation

The left chart compares population growth percentage-wise, while the right shows absolute increases.
Together, they reveal that outer-metropolitan LGAs such as Melton, Wyndham, and Casey lead both in relative and total growth, reflecting Victoria’s ongoing suburban expansion.


# Demographics <!-- Page 3 -->


## Female Share by Age Group
### Chart

```{r fig.width=22, fig.height=10, fig.align='center'}

ggplot(demo_long, aes(x = age_group, y = female_pct, fill = region)) +
  geom_col(position = "dodge", width = 0.7) +
  geom_text(aes(label = paste0(round(female_pct, 1), "%")),
            position = position_dodge(width = 0.7),
            vjust = -0.25, size = 6, color = "black", fontface = "bold") +
  labs(
    title = "Female Share by Age Group: Melbourne vs Rest of Victoria",
    x = "Age Group",
    y = "Female Population (%)",
    fill = ""
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    expand = expansion(mult = c(0, 0.12))
  ) +
  scale_fill_manual(values = c("Melbourne" = "#FF6F61", "Rest of Vic" = "#00BFC4")) +
  theme_minimal(base_size = 24) +
  theme(
    plot.title = element_text(face = "bold", size = 28, hjust = 0.5),
    axis.text.x = element_text(size = 18, angle = 45, hjust = 1),
    axis.text.y = element_text(size = 18),
    axis.title = element_text(size = 20, face = "bold"),
    legend.position = "bottom",
    legend.text = element_text(size = 18),
    panel.grid.minor = element_blank()
  )

```

## Interpretation

### Interpretation

The chart shows that Melbourne consistently maintains a slightly higher female population share across most age groups compared to the rest of Victoria.  
The largest difference is seen among younger working-age groups (25–34 years), reflecting Melbourne’s attraction for education and job opportunities.  
Meanwhile, the Rest of Victoria records higher female shares in older age brackets, suggesting an aging regional demographic trend.